The Origins of Statutory Producer Control Legislation in New Zealand Agriculture 1914-1925

<p>This thesis investigates the origins of statutory producer control that emerged out of the Board of Trade (Wool Industry) Regulations 1921, The Meat Export Control Act, 1921-22 and The Dairy Produce Export Control Act, 1923. Most histories have traced the formation of statutory producer organisations to the economic conditions that prevailed during the British Government's 1915-1921 bulk purchase agreement with New Zealand known as the 'Imperial Commandeer' and the commodity price slump that marked the Commandeer's conclusion. Analysis of agricultural income series and output data suggest that the popular view of a 'boom' and 'slump' cycle is overplayed. This thesis argues that net real farm incomes remained relatively static during the Commandeer while output contracted. The slump cycle beginning in 1920/21 was particularly severe given the interaction of key demand and supply variables. The contemporary argument for statutory intervention may have been based on misconceptions in some cases, yet when the economic arguments are evaluated a strong case emerges. The central role played by W.F. Massey and his Reform Party Government is also important to an understanding of how this legislation came about.</p>

The Origins of Statutory Producer Control Legislation in New Zealand Agriculture 1914-1925

<p>This thesis investigates the origins of statutory producer control that emerged out of the Board of Trade (Wool Industry) Regulations 1921, The Meat Export Control Act, 1921-22 and The Dairy Produce Export Control Act, 1923. Most histories have traced the formation of statutory producer organisations to the economic conditions that prevailed during the British Government's 1915-1921 bulk purchase agreement with New Zealand known as the 'Imperial Commandeer' and the commodity price slump that marked the Commandeer's conclusion. Analysis of agricultural income series and output data suggest that the popular view of a 'boom' and 'slump' cycle is overplayed. This thesis argues that net real farm incomes remained relatively static during the Commandeer while output contracted. The slump cycle beginning in 1920/21 was particularly severe given the interaction of key demand and supply variables. The contemporary argument for statutory intervention may have been based on misconceptions in some cases, yet when the economic arguments are evaluated a strong case emerges. The central role played by W.F. Massey and his Reform Party Government is also important to an understanding of how this legislation came about.</p>

Understanding the difference in softness of Australian Soft Rolling Skin wool and conventional Merino wool

Softness is one of the key elements of textile comfort and is one of the main considerations when consumers make purchasing decisions. In the wool industry, softness can reflect the quality and value of wool fibers. There is verifiable difference in subjective softness between Australian Soft Rolling Skin (SRS) wool and conventional Merino (CM) wool, yet the key factors responsible for this difference are not yet well understood. Fiber attributes, such as crimp (curvature), scale morphology, ortho-to-cortex (OtC) ratio and moisture regain, may have a significant influence on softness performance. This study has examined these key factors for both SRS and CM wool and systematically compared the difference in these factors. There was no significant difference in the crimp frequency between these two wools; however, the curvature of SRS wool was lower than that of CM wool within the same fiber diameter ranges (below 14.5 micron, 16.5–18.5 micron). This difference might be caused by the lower OtC ratio for SRS wool (approximately 0.60) than for CM wool (approximately 0.66). The crystallinity of the two wools was similar and not affected by the change in OtC ratio. SRS wool has higher moisture regain than CM wool by approximately 2.5%, which could reduce the stiffness of wool fibers. The surface morphology for SRS wool was also different from that of CM wool. The lower cuticle scale height for SRS wool resulted in its smoother surface than CM wool. This cuticle height difference was present even when they both had similar cuticle scale frequency.

Vulpia myuros (annual fescue).

Vulpia myuros is an annual grass, native to much of Europe and parts of Asia, introduced to the USA, Australia and a number of other countries, and reported as invasive in Australia, the western USA and parts of the Pacific. It out competes native species in grasslands of the western US and is a significant agricultural weed. It forms dense swards and its shallow roots suppress growth of native grasses and forbs. Establishment of native plants is strongly hindered once it has become dominant; because it is a winter-annual, it grows rapidly in early spring, thus successfully competing with the slower-growing native perennial grasses. It is a problem weed in pastures and in direct-seed cropping systems. Infested hay can cause injury to livestock due to the sharp seeds. Seeds easily attach to animals and cause losses in the wool industry. Residues of degrading Vulpia plants affect growth of other species including crops.

RAW WOOL PREPARATION IN UZBEKISTAN

The article provides information on the properties of wool harvested in the Republic of Uzbekistan. The natural properties of wool, its thickness and length are analyzed. The article describes the process of dividing raw wool into classes and grades when sending wool from processing enterprises to primary processing enterprises. The classification of woolen raw materialsby classes and varieties is given. The main supplier of raw materials for the wool industry is animal husbandry. Uzbekistan has a number of opportunities, geographic and economic factors for the development of this industry. On the basis of organizing the processing of wool fiber, it will be possible to create new jobs

Evaluation of a new portable device that measures diameter of animal fibres

Increasing production of animal fibres has increased the need for a portable instrument that measures fibre diameter and associated characteristics with precision and accuracy. This research evaluated a new portable fibre tester (PFT) by measuring the diameter and related characteristics of tops and scoured fibres of wool, alpaca, and vicuña. The PFT was constructed with integrated mechanical, optical, electronic, and informatic components. Textile tops of sheep wool, alpaca fibres, and mohair goat fibres were used as standard references to calibrate the PFT and determine its accuracy and precision. The results were compared with those from a wool industry standard instrument (OFDA2000) that uses similar technology. The PFT had high accuracy (-0.01, -0.12, and -0.01 μm) for average fibre diameter (AFD) of wool, alpaca, and mohair fibres, respectively. Deviations of standard tops (ST) were within industry-accepted tolerance ranges. Standard errors, indicating precision, were low, ranging from 0.07 to 0.25 μm, 0.02 to 0.44 μm, and 0.09 to 0.024 μm, for wool, alpaca, and mohair fibre tops, respectively. The correlations of measurements of AFD from the two instruments were 0.99 for wool, alpaca, and mohair fibres, but lower for vicuña fibres (0.82). No evidence of bias was observed. Therefore, the PFT may be used as an alternative instrument for measuring fibre diameter and quantifying variation in diameter of wool, mohair, and alpaca fibres. The PFT has appeal for use in the field for practical animal selection and fleece classification based on fibre characteristics. Keywords: alpaca, fibre diameter, mohair, vicuña, wool

Animal fiber imagery classification using a combination of random forest and deep learning methods

Feature extraction is a key step in animal fiber microscopic images recognition that plays an important role in the wool industry and textile industry. To improve the accuracy of wool and cashmere microscopic images classification, a hybrid model based on Convolutional Neural Network (CNN) and Random Forest (RF) is proposed for automatic feature extraction and classification of animal fiber microscopic images. First, use CNN to learn the representative high-level features from animal fiber images, then add dropout layers to avoid over-fitting. And the backward propagation algorithm are used to optimize the CNN structure. Random forest, which is robust and has strong generalization ability, is introduced for the classification of animal fiber microscopic images to obtain the final results. The study shows that, the proposed method has better generalization performance and higher classification accuracy than other classification methods.